Classification of surface EMG signal with fractal dimension

• Published in: Journal of Zhejiang University. B. Science Vol. 6; no. 8; pp. 844 - 848
• Main Author: 胡晓 王志中 任小梅
• Format: Journal Article
• Language: English
• Published: China Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China 01.08.2005; Zhejiang University Press
• Subjects: Algorithms; Diagnosis, Computer-Assisted - methods; Electromyography - methods; Fractals; Humans; Muscle Contraction - physiology; Pattern Recognition, Automated - methods; Plant & Animal Sciences and Biotechnology; Signal Processing, Computer-Assisted; 信噪比; 图象参数; 肌电图学; 表面信号; 高频噪声; GP algorithm; Fractal dimension; Correlation dimension; Surface EMG signal; Self-similarity
• ISSN: 1673-1581; 1009-3095; 1862-1783
• DOI: 10.1631/jzus.2005.B0844

Surface EMG （electromyography） signal is a complex nonlinear signal with low signal to noise ratio （SNR）. This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination （FS） or forearm pronation （FP）. After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal dimension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can represent different patterns of surface EMG signals.